The Study of Factors Influencing the Adoption of mHealth Applications among the Consumers of HealthServices
Main Article Content
Abstract
Mobile-enabled health technologies let individuals obtain clinical guidance and assessments without an in-person visit and give public health agencies a powerful channel for distributing programmes throughout a community. This study investigates how users’ motivation to avoid perceived threats interacts with their attitudes to shape intentions to adopt mobile-health (mHealth) apps. To capture both acceptance and avoidance dynamics, the research unites the Technology Acceptance Model (TAM) with Technology Threat Avoidance Theory (TTAT). In the integrated framework, threat- and coping-appraisal processes feed avoidance motivation, whereas perceived ease of use and perceived usefulness foster a favourable attitude. The hypothesised paths are assessed with Partial Least Squares Structural Equation Modelling (PLS-SEM). By highlighting the most influential mHealth determinants from the perspective of key stakeholders, the findings offer actionable guidance for practitioners and policymakers overseeing mHealth roll-outs.
References
i. Addotey-Delove, M., Scott, R. E., & Mars, M. (2022). A healthcare workers’ mHealth adoption instrument for the developing world. BMC Health Services Research, 22(1), 1225. https://doi.org/10.1186/s12913-022-08592-0
ii. Agarwal, S., Rana, M. S., & Sharma, P. (2023). The health impact of mHealth interventions in India: A systematic review. Online Journal of Public Health Informatics, 15, e50927. https://doi.org/10.2196/50927
iii. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
iv. Alam, M. M. D., Alam, M. Z., Rahman, S. A., & Taghizadeh, S. K. (2021). Factors influencing mHealth adoption and its impact on mental wellbeing during COVID-19 pandemic: A SEM-ANN approach. Journal of Biomedical Informatics, 116, 103722. https://doi.org/10.1016/j.jbi.2021.103722
v. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
vi. Black, J., & Cherrington, J. (2022). Posthuman to Inhuman: mHealth Technologies and the Digital Health Assemblage. Theory & Event, 25, 726. https://doi.org/10.1353/tae.2022.0039
vii. Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
viii. Chiu, W., Won, D., & Chen, J. (2025). Older adults’ adoption behavior of mobile health (mHealth) apps: Integrating technology readiness with protection motivation theory. Asia Pacific Journal of Marketing and Logistics. https://doi.org/10.1108/APJML-10-2024-1468
ix. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
x. Deng, H., Zhang, J., & Chen, H. (2024). Quality factors affecting continued use of mobile health apps in ethnic minority regions of Southwest China using PLS-SEM and ANN. Scientific Reports, 14(1), 25469. https://doi.org/10.1038/s41598-024-75410-4
xi. DeSouza, S. I., Rashmi, M. R., Vasanthi, A. P., Joseph, S. M., & Rodrigues, R. (2014). Mobile phones: The next step towards healthcare delivery in rural India? PLOS ONE, 9(8), e104895. https://doi.org/10.1371/journal.pone.0104895
xii. Fan, S., Jain, R. C., & Kankanhalli, M. S. (2023). A comprehensive picture of factors affecting user willingness to use mobile health applications. arXiv:2305.05962
xiii. Ferreira, J. B., & Caldeira, T. A. (2024). MT48 Enhanced Healthcare Through the Adoption of Mobile Health Apps. Value in Health.
xiv. Garavand, A., Samadbeik, M., Nadri, H., Rahimi, B., & Asadi, H. (2019). Effective factors in adoption of mobile health applications between medical sciences students: Application of the UTAUT model. Methods of Information in Medicine, 58(4-5), 131–139. https://doi.org/10.1055/s-0040-1701607
xv. Goel, A., & Taneja, A. (2023). User motivation and adoption of mHealth services: A multi-group analysis of generational cohorts. Telematics and Informatics, 81, 102046. https://doi.org/10.1016/j.tele.2023.102046
xvi. Goel, A., & Taneja, U. (2023). Mobile health applications for health-care delivery: Trends, opportunities, and challenges. Journal of Public Health. https://doi.org/10.1007/s10389-023-02165-z
xvii. Guo, X., Han, X., Zhang, X., Dang, Y., & Chen, C. (2015). Investigating m-health acceptance from a Protection Motivation Theory perspective. Telemedicine and e-Health, 21(8), 661–669.
xviii. Healthcare at Your Fingertips: Case of mHealth in India. Centre For Civil Society. (n.d.).
xix. Huang, B., Estai, M., Schultz, E. C., & Shenouda, M. (2025). Perspectives of Front-Line Clinicians… Smartphone-Based Photography. University of Minnesota Digital Conservancy.
xx. India: Mobile phone internet users 2050 | Statista. (n.d.)
xxi. Liang, H., & Xue, Y. (2010). Understanding Security Behaviors in Personal Computer Usage: A Threat Avoidance Perspective. Journal of the Association for Information Systems, 11(7).
xxii. Liu, J. Y. W., Liu, Y., & Lee, K. T. (2023). Determinants of intention to continue mHealth services… Technological Forecasting and Social Change, 187, 122299.
xxiii. (Duplicate of above entry xxii.)
xxiv. Mouloudj, K., et al. (2023). Factors influencing the adoption of digital health apps: An extended TAM. IGI Global.
xxv. Palas, J. U., et al. (2022). Factors influencing the elderly’s adoption of mHealth. BMC Medical Informatics and Decision Making, 22(1), 191.
xxvi. PricewaterhouseCoopers. (n.d.). mHealth and the Healthcare industry.
xxvii. Rahman, A., & Uddin, J. (2025). Revealing factors influencing mHealth adoption intention… Digital, 5(2), 9.
xxviii. Rajak, M., & Shaw, K. (2021). An extension of technology acceptance model for mHealth user adoption. Technology in Society, 67, 101800.
xxix. Riley, W. T., et al. (2011). Health behavior models in the age of mobile interventions. Translational Behavioral Medicine, 1(1), 53–71.
xxx. Silva, P. (2015). Davis’ Technology Acceptance Model (TAM) (1989). IGI Global.
xxxi. Swain, S., Muduli, K., Kumar, A., & Luthra, S. (2023). Analysis of barriers of mHealth adoption… International Journal of Industrial Engineering and Operations Management, 6(2), 85–116.
xxxii. Tajudeen, F. P., et al. (2022). Mobile Technologies and Healthy Ageing: A Bibliometric Analysis… International Journal of Human–Computer Interaction, 38(2), 118–130.
xxxiii. Tian, X. F., & Wu, R. Z. (2022). Determinants of the mobile health continuance intention… IJERPH, 19(16), 9980.
xxxiv. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
xxxv. Weichelt, B. P., et al. (2024). Provider adoption of mHealth in rural patient care. JMIR Human Factors, 11, e55443.
xxxvi. Yang, M., et al. (2024). Predicting m-health acceptance… Scientific Reports, 14(1), 339.
xxxvii. Zhou, T. (2011). Understanding mobile Internet continuance usage from UTAUT and flow. Information Development, 27(3), 207–218.
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